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A code for "Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking" in ACL2019

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## Unsupervised Neural Single-Document Summarization of Reviews via Learning Latent Discourse Structure and its Ranking

Corresponding paper:

https://arxiv.org/abs/1906.05691  
Masaru Isonuma, Juncihiro Mori and Ichiro Sakata (The University of Tokyo)  
Accepted in ACL 2019 as a long paper

### Requirements

Python 3.6  
Tensorflow 1.8.0

### Usage

#### Preprocessing

The pre-processed Amazon Sports & Outdoors review data can be downloaded at:  
Put it at `/path/to/data`

To use other dataset, run:

```
python create_dataframe.py --input_path /path/to/gzip/file --output_path /path/to/dataframe
python preprocess_data.py --input_path /path/to/dataframe --output_path /path/to/data --word_vec_path /path/to/vec
```

Other dataset can be downloaded at http://jmcauley.ucsd.edu/data/amazon/  
Put raw gzip file at `/path/to/gzip/file`  
Also, download the pretrained FastText word vector file `crawl-300d-2M.vec` from the url below and put it at /path/to/vec  
https://fasttext.cc/docs/en/english-vectors.html

#### Training

To train your model, run:

```
python cli.py --mode train --data /path/to/data --modeldir /dir/of/model
```

Parameters and logs are saved in `/path/to/a/model/directory`  
The hyper parameters can be chaneged as described in `cli.py`

#### Evaluation

To write out and evaluate the summaries generated by your model, run:

```
python cli.py --mode eval --data /path/to/data --modeldir /dir/of/model --refdir /dir/of/refs --outdir /dir/of/outputs
```

References and output summaries are respectively saved in `/dir/of/refs` and `/dir/of/outputs`  
The same hyper parameters must be set as used for training

strsum

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